Clickbait detection using swarm intelligence

Abstract

Clickbaits are the articles containing catchy headlines which lure the reader to explore full content, but do not have any useful information. Detecting clickbaits solely by the headline without opening the link, can serve as a utility for users over internet. This can prevent their time from useless surfing caused by exploring clickbaits. In this paper Ant Colony Optimization, a Swarm Intelligence (SI) based technique has been used to detect clickbaits. In comparison with algorithms used in the past, this SI based technique provided a better accuracy and a human interpretable set of rules to classify clickbaits. A maximum accuracy of 96.93% with a set of 20 classification rules was obtained using the algorithm.